code: def test_bts_split_logic():
      X, y, cv, df_etalon_stat, _ = _prepare_data()
      df_stat = []
      for _, (train_idx, test_idx) in enumerate(cv.split(X, y)):
          df_stat.append(
              [
                  len(train_idx),
                  min(train_idx),
                  max(train_idx),
                  len(test_idx),
                  min(test_idx),
                  max(test_idx),
              ]
          )
      columns = [
          "train_len",
          "train_min",
          "train_max",
          "test_len",
          "test_min",
          "test_max",
      ]
      df_stat = pd.DataFrame(df_stat, columns=columns)
  
      assert df_stat.equals(df_etalon_stat)
  
file_path: upgini/tests/test_blocked_time_series.py
imports: import re
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import cross_val_score
  from upgini.resource_bundle import bundle
  from upgini.utils.blocked_time_series import BlockedTimeSeriesSplit
label_desc: Use image node_id 2 for calling a global function with example usage: test_bts_split_logic() without return types